RAN D 89 9 13 089

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A RAND NOTE
Knowledge-Based Simulation:
An Interim Report
Jeff Rothenberg, Sanjai Narain,
Randall Steeb, Charlene Hefley,
Norman Z. Shapiro
SnJuly 1989
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The research described in this report was sponsored by the
Defense Advanced Research Projects Agency under RAND's
National Defense Research Tnstitute, a Federally Funded
Research and Developnient Cen~ter 'upported by the Office
of the Secretary of DefenF.e, Contract No. MDA903-85-C-0030.
The RAND Publication Series: The Report is the principal publication doc-
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Knowledge-Based Simulation: An Interim Report Interim
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Jeff Rothenberg, Saniai Narain, Randall Steeb,
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14. SUPPt.,Eih4CNARV NOTES
Simulation,
Artific'.al Tntalligenc -),
Models
Reasoning.
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Simulation Is a modeling technique that is
widely used in such areas as policymaking,
Manufacturing, com.puter system design, and
military analysis. In spite of its
widespread use, current simulation
technology suffers from a number of serious
drawbacks that limit its power,
applicability, and credibility. The
Knowledge-Based Simulation (KBSim) project
at RAND is engaged in research aimed at
producing a new-generation simulation
environment that will greatly extend the
capabilities of current simulation
technology, particularly as applied to
analytic modeling. The KBSim project has
focused on reasoning in-simulation. /-%
representation of multiple relations among ' -
simulated entities, highly interactive
interfaces, sensitivity analysis, variation
of the aggregation level of a model, and
the modeling of "soft" concepts such as
initiative. This Note describes the KBSim
project, discusses its goals, and presents
an overview of its progress.
UNCLASSIFIED
1CMUNIT? CLASSIOP CA rION O F TmtS OD agEd.W h t f- .)
A RAND NOTE
N-2897-DARPA
Knowledge-Based Simulation:
An Interim Report
Jeff Rothenberg, Sanjal Narain,
Randall Steeb, Charlene Hefley,
Norman Z. Shapiro
July 1989
Prepared for
The Defense Advanced Research Projects Agency
RAND
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED
- Ini -
PREFACE
Simulation is a modeling technique that is widely used in such areas as
policymaking, manufacturing, computer system design, and military analysis.
Despite its ubiquity, current simulation technology suffers from a number of
serious drawbacks that limit its power, applicability, and credibility. This Note
describes the Knowledge-Based Simulation project at The RAND Corporation,
which is being conducted for the Information Science and Technology Office of the
Defense Advanced Research Projects Agency (DARPA), under RAND's National
Defense Research Institute (NDRI). The NDRI is a Federally Funded Research
and Developmnt Center sponsored by the Office of the Secretary of Defense. This
project is investigating ways of using knowledge-based techniques to produce a
new generation of simulation environments that allow building more powerful,
comprehensible, and reusable simulation models. The Note discusses the
problems with current simulrt;; ,n paradigms, identifies research issues intended
to address these problems, and presents interim results and directions for
continued research. It should be of value to developers of simulation environments
and researchers concerned with fundamental modeling issues. It should also be of
interest to modelers who build analytic simulations and to analysts who use such
simulations.
The work discussed here has been coordinated with several other ongoing
research projects at RAND, notably the Intelligent Database (IDB) project and the
Advanced Geographic Environment (AGE) project. Related RAND research is
described in McArthur, Klahr, and Narain (1984); Steeb et al. (1986); and
Cammarata, Gates, and Rothenberg (1988).
Accesion For
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SUMMARY
Discrete-state simulation is a modeling technique that is particularly
useful for analyzing domains involving complex temporal phenomena whose
interactions defy complete mathematical analysis. Simulation models typically
consist of descriptions of real-world entities to be modeled and "first-order"
interactions among these entities; running the simulation to observe the effects of
these first-order interactions often reveals higher level interactions and effects
that were not known in advance, answering questions of the form "What if. . .
This traditional view of simulation (and the technology that supports it) is severely
limited in its ability to answer other kinds of questions of at least equal value; iý.i s
also limited in its ability to represent a wide range of phenomena in ways that are
comprehensible to both model builders and users.
The lack of explicit representation of domain knowledge in simulations
makes it difficult to verify the correctness of their underlying models and makes
them hard to comprehend. Recent advances in object-oriented simulation
languages, rule-oriented approaches, logic programming, automated inferencing,
and interactive graphics are facilitating a new generation of simulation
environments. The synergy of these techniques promises to revolutionize
simulation, transforming it into something far more powerful, more useful, and
more believable. It is th is s3nergy that we call "knowledge-based simulation."
The Knowledge-Based Simulation ("KBSim") project at The RAND
Corporation is engaged in research aimed at producing a new-generation
simulation environment that will greatly extend the capabilities of current
simulation technology, particularly as applied to analytic modeling. This work
has focused on the following areas:
"* Reasoning in simulation
"* Representation of multiple relations among simulated entities
"• Highly interactive interfaces
"* Sensitivity analysis
- vi -
"* Variation of the aggregation level of a model
"• Modeling of "soft" concepts (such as initiative)
This Note describes the KBSim project, discusses its goals, and presents an
overview of its progress to date.
We have produced a number of papers summarizing our research results in
several areas. We have also produced several prototype facilities that demonstrate
new capabilities. The following summarizes these accomplishments and
discusses them briefly.
Reasoning in Simulation
• ROSS-in-Prolog and DMOD formalisms
- Proof-of-concept demonstration
Multiple Relations (and Extended Objects)
* Analysis of object-oriented simulation paradigm deficiercies
- Winter Simulation Conference paper (Rothenberg, 1986)
- KBSim Progress Report (this RAND Note)
Highly Interactive Interfaces
"* Graphics-delta, dependencies, demons
- Extended ROSS ("XROSS") capability
- Winter Simulation Confer 3nce paper (Cammarata, Gates, and
Rothenberg, 1987)
"* Graphics environment
- Graphics-in-LISP (GIL) capability using GKS graphics standard
Sensitivity Analysis
New propagative approach
- Analytic evaluation of the new approach (LISP)
- Initial prototype computational environment (LISP)
- RAND Note (in preparation)
Variation of the Aggregation Level of a Model
* Variable aggregation
- Demonstration
- RAND Note (in preparation)
-vii -
Modeling oi "Soft" Concepts
0 Identification of key issues and approaches
We have identified a wide range of issues and shortcomings in current
object-oriented simulations, based on extensive experience with this approach at
RAND and elsewhere. This has led to the initiation of the new language design
effort to be undertaken by the Transfer of Simulation Technology project at RAND.
We have implemented a subset of the ROSS language in Prolog as a proof of
the feasibility of using logic programming in an object-oriented style. This
demonstration formed the basis for the new DMOD language with which we are
pursuing these ideas further.
We have designed and implemented solutions to several kinds of artifacts
in simulations, having to do with the difficulty of keeping graphic displays up to
date without excessive coding effort on the part of the modeler (the "graphics-delta"
technique), with representing dependencies among simulation entities, and with
representing "autonomous" behavior in a simulation. These have resulted in an
interim, extended version of the ROSS language, referred to as XROSS (described
in Cammarata, Gates, and Rothenberg, 1987 and 1988).
We have built a graphics environment using the GKS graphics standard
and SunWindows, and have integrated this with the XROSS extensions described
above.
We have implemented a LISP program that analyzes the expected "payoff'
from our new sensitivity analysis approach; this led us to conclude that a
computational environment based on this approach would be well worthwhile. We
have implemented an initial prototype of such an environment.
We have produced a simplified demonstration that exhibits variable
resolution, i.e., varies the aggregation of simulated entities such as companies
and platoons. This also uses our graphics environment and XROSS extensions, as
well as incorporating the beginnings of a planning facility for modeling
decisionmaking.
In addition, we have explored the modeling of "soft concepts" (such as a
commander's use of initiative) and have identified some promising approaches to
integrating such models into battlefield simulations.